What problems can be solved with Data Science?

Added on:
10 August 2022
Daniel Szulc

Many companies have already successfully implemented smart solutions in marketing departments, using data and its advanced analytics. The use of data science solutions increases the efficiency of operations, reduces costs, optimizes budgets and improves ROI. Wanting to remain competitive, other companies need to catch up with the leaders as soon as possible. Where to start? How do you effectively use the data available in your organization? Learn the proven approach of Data Science Logic’s experts.

Customer segmentation

Traditional segmentation approaches are limited as to the number of variables that can be considered. For example, a typical RFM (from recency, frequency, monetary value) considers only three variables. Machine learning methods can segment consumers based on a virtually unlimited number of dimensions. They take into account not only demographic data, but also behavioral data related to both purchasing behavior and consumer interactions at various touchpoints (e.g., web, mailings, app). As a result of segmentation, “personae” are created – typical representatives of the segment, whose characteristics allow to differentiate the approach and plan optimal actions for them.

Prediction of customer value

Machine learning models can predict with a high degree of accuracy the value of a customer throughout its lifecycle. This is possible from the very first (even residual) data about the consumer’s relationship with the company. Of course, the more data, the more accurate the prediction. However, already the initial prediction allows you to decide how much it pays to invest in the relationship with a given customer. This allows you to focus your attention and budget on the most profitable customers.

Anti-churn measures

Machine learning makes it possible to pinpoint customers at risk of leaving. This makes it possible to identify and prioritize customers against whom action needs to be taken. Combined with predictive models of customer value, it is possible to make an optimal decision on how much to invest in retaining a given customer (e.g., in the form of a discount). Using direct communication models, it is possible to find the optimal timing, channel and content of an anti-churn message. The model can also identify a good enough offer for a given customer. For example, if a customer decides to stay after receiving a 5% discount, there is no need to offer him a 15% discount. The predictive model can thus contribute to significant budget savings.

Direct communication planning

Predictive modeling significantly supports the process of preparing and planning marketing communications – not only anti-churn. On the basis of data on consumer interactions with the company, it is possible to predict the positive reaction of a given customer to a specific content, offer, moment and dispatch channel. This allows optimization of the budget – for example, choosing a cheaper channel if the expected effect is similar, or improving the consumer experience – less spam, more tailored content, the most convenient communication channel.

Content analysis and recommendations

Advanced predictive models based on so-called deep machine learning are able to process not only numerical data, but also image, text, sound or video. This makes it possible to predict the effect that the content sent in communications will have on a particular customer. This makes it possible to choose the right content and title of the email, the optimal layout and graphics.

Analysis of the incremental effect of promotion

Promotions and price reductions are an important budget item. Not surprisingly, questions arise about their effectiveness and impact on sales. A simple analysis is often not enough. Reducing the price of a product almost always increases its sales. Just by observing the dynamics of sales during the promotional period, one can conclude that it has worked favorably. Meanwhile, it is necessary to find the answer to the question of how much sales would have been if there had not been this promotion. Only by comparing these two values (actual sales and hypothetical sales without the promotion) can the effect of the promotion be assessed. Advanced data science models are capable of estimating baseline sales with a high degree of accuracy, also taking into account such factors as seasonality, cannibalization, weather variability, calendar effects.

The examples presented are just some of the problems that are already being solved with the help of data science, and using the full potential of data can significantly affect a company’s position in the market.